ES-HPC-MPC: Exponentially Stable Hybrid Perception Constrained MPC for Quadrotor with Suspended Payloads
Luis F. Recalde, Mrunal Sarvaiya, Giuseppe Loianno, Guanrui Li
AI summary
Problem
Existing control methods for cable-suspended quadrotors assume the cable is always taut, causing instability and loss of visual tracking when real-world disturbances trigger slack-to-taut transitions.
Approach
ES-HPC-MPC integrates dynamically updated Exponentially Stabilizing Control Lyapunov Functions for stability and Control Barrier Functions to keep the payload within the camera's field of view across both slack and taut modes.
Key results
- Dynamically updated ES-CLFs enforce exponential stability across hybrid modes
- CBF constraints guarantee continuous payload visibility within the camera's field of view
- Simulation and real-world validation confirm stability and perception safety during slack-to-taut transitions
- Outperforms baselines in tracking accuracy and constraint satisfaction under disturbances
Why it matters
Enables safer, more reliable autonomous aerial transportation for disaster response and logistics by handling real-world cable slackness without crashing or losing visual tracking.
Abstract
Aerial transportation using quadrotors with cable- suspended payloads holds great potential for applications in disaster response, logistics, and infrastructure maintenance. How- ever, their hybrid and underactuated dynamics pose significant control and perception challenges. Traditional approaches often assume a taut cable condition, limiting their effectiveness in real-world applications where slack-to-taut transitions occur due to disturbances. We introduce ES-HPC-MPC, a model predictive control framework that enforces exponential stability and perception-constrained control under hybrid dynamics. Our method leverages Exponentially Stabilizing Control Lyapunov Functions (ES-CLFs) to enforce stability during the tasks and Control Barrier Functions (CBFs) to maintain the payload within the onboard camera’s field of view (FoV). We validate our method through both simulation and real-world experiments, demonstrating stable trajectory tracking and reliable payload perception. We validate that our method maintains stability and satisfies perception constraints while tracking dynamically infeasible trajectories and when the system is subjected to hybrid mode transitions caused by unexpected disturbances.